Recent Hidden Markov Models for Lower Limb Locomotion Activity Detection and Recognition using IMU sensors

Author : Haoyu Li

Abstract

The thesis context is that of the quantified self, a movement born in California that consists in getting to know oneself better by measuring data relating to one’s body and activities. The research work consisted in developing algorithms for analyzing signals from an IMU (Inertial Measurement Unit) sensor placed on the leg to recognize different movement activities such as walking, running, stair climbing… These activities are recognizable by the shape of the sensor’s acceleration and angular velocity signals, both tri-axial, during leg movement and gait cycle.

To address the problem, the thesis work resulted in the construction of a particular hidden Markov chain, called semi-triplet Markov chain (semi-TMC), which combines a semi-Markov model in a Triplet Markov model. This semi-TMC is both adapted to the nature of the gait cycle, and adapted to the sequence of activities as it can be carried out in daily life. To adapt the model parameters to the differences in human morphology and behavior, we have developed algorithms for estimating parameters both off-line and on-line.

To establish the classification and learning performance of the algorithms, we conducted experiments on the basis of recordings collected during the thesis and on public recordings. The results are systematically compared with state-of-the-art algorithms.